Level: Intermediate to Advanced
Prerequisite: None
Data science is the key to business success in the information economy. This course will teach you about best practices in deploying a data science capability for your organization. Technology is the easy part—the hard part is creating the right organizational and delivery framework in which data science can succeed.
Stephen Brobst will discuss the necessary skill sets for a successful data scientist and the environment that will allow them to thrive. He will draw a strong distinction between “data R&D” and “data product” capabilities within an enterprise and speak to the different skill sets, governance, and technologies needed across these areas. Brobst will also explore the use of open data sets and open source software tools to enable best results from data science in large organizations, as well as the many pitfalls and how to avoid them. Best practices in choosing AI/ML approaches will be discussed, along with pitfalls related to model bias, training set selection, goal setting, and model half-life management.
You Will Learn
- How to innovate using data science in the age of AI and machine learning
- The most common mistakes made when performing advanced analytics
- How to deploy a data lake, data R&D, and data product capabilities within your organization
- Machine learning, deep learning, and generative artificial intelligence concepts and which use cases work best for which techniques
Geared To
- Data scientists
- Citizen data scientists
- Power users
- BI analysts
- Business and data analysts
- Solution architects
- Big data architects
- Data warehousing specialists
- Business and IT leaders
- Analytics project managers
- Analytics program managers